stFemale)Geiser C. Challco geiser@alumni.usp.br
env <- "stFemale"
gender <- "women"
to_remove <- c('S11')
sub.groups <- c("country","age","ed.level","intervention",
"country:age","country:ed.level","country:intervention",
"age:intervention","ed.level:intervention",
"country:age:intervention","country:ed.level:intervention")dat <- read_excel("../data/data-without-outliers.xlsx", sheet = "perform-env.gender-descriptive")
dat <- dat[!dat$study %in% to_remove, ]
leg <- read_excel("../data/data-without-outliers.xlsx", sheet = "legend")## New names:
## • `` -> `...10`leg <- leg[!leg$study %in% to_remove, ]
idx.e <- which(dat$env == env & dat$gender == gender)
idx.c <- which(dat$env == "control" & dat$gender == gender)
data <- data.frame(
study = dat$study[idx.c],
n.e = dat$N[idx.e], mean.e = dat$M[idx.e], sd.e = dat$SD[idx.e],
n.c = dat$N[idx.c], mean.c = dat$M[idx.c], sd.c = dat$SD[idx.c]
)
for (cgroups in strsplit(sub.groups,":")) {
data[[paste0(cgroups, collapse = ":")]] <- sapply(data$study, FUN = function(x) {
paste0(sapply(cgroups, FUN = function(namecol) leg[[namecol]][which(x == leg$study)]), collapse = ":")
})
}
data[["lbl"]] <- sapply(data$study, FUN = function(x) leg$Note[which(x == leg$study)])m.cont <- metacont(
n.e = n.e, mean.e = mean.e, sd.e = sd.e, n.c = n.c, mean.c = mean.c, sd.c = sd.c,
studlab = lbl, data = data, sm = "SMD", method.smd = "Hedges",
fixed = F, random = T, method.tau = "REML", hakn = T, title = paste("Performance for",gender,"in",env)
)
summary(m.cont)## Review: Performance for women in stFemale
##
## SMD 95%-CI %W(random)
## S1 0.0337 [-0.5959; 0.6633] 10.9
## S2 -0.0214 [-0.5446; 0.5018] 14.8
## S3 -0.5787 [-1.3128; 0.1555] 8.3
## S4 0.6745 [-0.1048; 1.4539] 7.4
## S5 0.2265 [-0.3774; 0.8305] 11.7
## S6 0.4025 [-0.3214; 1.1264] 8.5
## S7 0.5444 [-0.0754; 1.1641] 11.2
## S8: Conducted by BNU -0.1893 [-0.8121; 0.4335] 11.1
## S9: Albuquerque, et al. (2017) -0.4198 [-1.1460; 0.3064] 8.5
## S10: Only use prompt msgs 0.1888 [-0.5722; 0.9498] 7.8
##
## Number of studies combined: k = 10
## Number of observations: o = 366
##
## SMD 95%-CI t p-value
## Random effects model 0.0823 [-0.1890; 0.3537] 0.69 0.5098
##
## Quantifying heterogeneity:
## tau^2 = 0.0169 [0.0000; 0.4121]; tau = 0.1299 [0.0000; 0.6420]
## I^2 = 20.0% [0.0%; 60.5%]; H = 1.12 [1.00; 1.59]
##
## Test of heterogeneity:
## Q d.f. p-value
## 11.25 9 0.2587
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.cont, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = country, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for women in stFemale
##
## SMD 95%-CI %W(random) country
## S1 0.0337 [-0.5959; 0.6633] 10.9 Brazil
## S2 -0.0214 [-0.5446; 0.5018] 14.8 Brazil
## S3 -0.5787 [-1.3128; 0.1555] 8.3 Brazil
## S4 0.6745 [-0.1048; 1.4539] 7.4 Brazil
## S5 0.2265 [-0.3774; 0.8305] 11.7 Brazil
## S6 0.4025 [-0.3214; 1.1264] 8.5 Brazil
## S7 0.5444 [-0.0754; 1.1641] 11.2 Brazil
## S8: Conducted by BNU -0.1893 [-0.8121; 0.4335] 11.1 China
## S9: Albuquerque, et al. (2017) -0.4198 [-1.1460; 0.3064] 8.5 Brazil
## S10: Only use prompt msgs 0.1888 [-0.5722; 0.9498] 7.8 Brazil
##
## Number of studies combined: k = 10
## Number of observations: o = 366
##
## SMD 95%-CI t p-value
## Random effects model 0.0823 [-0.1890; 0.3537] 0.69 0.5098
##
## Quantifying heterogeneity:
## tau^2 = 0.0169 [0.0000; 0.4121]; tau = 0.1299 [0.0000; 0.6420]
## I^2 = 20.0% [0.0%; 60.5%]; H = 1.12 [1.00; 1.59]
##
## Test of heterogeneity:
## Q d.f. p-value
## 11.25 9 0.2587
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## country = Brazil 9 0.1162 [-0.1849; 0.4173] 0.0238 0.1542 10.44 23.3%
## country = China 1 -0.1893 [-0.8121; 0.4335] -- -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.79 1 0.3738
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = age, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for women in stFemale
##
## SMD 95%-CI %W(random) age
## S1 0.0337 [-0.5959; 0.6633] 10.9 adolescent
## S2 -0.0214 [-0.5446; 0.5018] 14.8 adolescent
## S3 -0.5787 [-1.3128; 0.1555] 8.3 adolescent
## S4 0.6745 [-0.1048; 1.4539] 7.4 adult
## S5 0.2265 [-0.3774; 0.8305] 11.7 adult
## S6 0.4025 [-0.3214; 1.1264] 8.5 adult
## S7 0.5444 [-0.0754; 1.1641] 11.2 adult
## S8: Conducted by BNU -0.1893 [-0.8121; 0.4335] 11.1 unknown
## S9: Albuquerque, et al. (2017) -0.4198 [-1.1460; 0.3064] 8.5 no-restriction
## S10: Only use prompt msgs 0.1888 [-0.5722; 0.9498] 7.8 adolescent
##
## Number of studies combined: k = 10
## Number of observations: o = 366
##
## SMD 95%-CI t p-value
## Random effects model 0.0823 [-0.1890; 0.3537] 0.69 0.5098
##
## Quantifying heterogeneity:
## tau^2 = 0.0169 [0.0000; 0.4121]; tau = 0.1299 [0.0000; 0.6420]
## I^2 = 20.0% [0.0%; 60.5%]; H = 1.12 [1.00; 1.59]
##
## Test of heterogeneity:
## Q d.f. p-value
## 11.25 9 0.2587
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## age = adolescent 4 -0.0759 [-0.5432; 0.3914] 0 0 2.42 0.0%
## age = adult 4 0.4401 [ 0.1343; 0.7459] 0 0 0.95 0.0%
## age = unknown 1 -0.1893 [-0.8121; 0.4335] -- -- 0.00 --
## age = no-restriction 1 -0.4198 [-1.1460; 0.3064] -- -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 13.94 3 0.0030
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = ed.level, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for women in stFemale
##
## SMD 95%-CI %W(random) ed.level
## S1 0.0337 [-0.5959; 0.6633] 10.9 upper-secundary
## S2 -0.0214 [-0.5446; 0.5018] 14.8 upper-secundary
## S3 -0.5787 [-1.3128; 0.1555] 8.3 upper-secundary
## S4 0.6745 [-0.1048; 1.4539] 7.4 higher-education
## S5 0.2265 [-0.3774; 0.8305] 11.7 higher-education
## S6 0.4025 [-0.3214; 1.1264] 8.5 higher-education
## S7 0.5444 [-0.0754; 1.1641] 11.2 unknown
## S8: Conducted by BNU -0.1893 [-0.8121; 0.4335] 11.1 unknown
## S9: Albuquerque, et al. (2017) -0.4198 [-1.1460; 0.3064] 8.5 unknown
## S10: Only use prompt msgs 0.1888 [-0.5722; 0.9498] 7.8 upper-secundary
##
## Number of studies combined: k = 10
## Number of observations: o = 366
##
## SMD 95%-CI t p-value
## Random effects model 0.0823 [-0.1890; 0.3537] 0.69 0.5098
##
## Quantifying heterogeneity:
## tau^2 = 0.0169 [0.0000; 0.4121]; tau = 0.1299 [0.0000; 0.6420]
## I^2 = 20.0% [0.0%; 60.5%]; H = 1.12 [1.00; 1.59]
##
## Test of heterogeneity:
## Q d.f. p-value
## 11.25 9 0.2587
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## ed.level = upper-secundary 4 -0.0759 [-0.5432; 0.3914] 0 0 2.42 0.0%
## ed.level = higher-education 3 0.3970 [-0.1540; 0.9481] 0 0 0.79 0.0%
## ed.level = unknown 3 -0.0029 [-1.2549; 1.2491] 0.1440 0.3795 4.59 56.5%
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 6.30 2 0.0429
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = intervention, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for women in stFemale
##
## SMD 95%-CI %W(random)
## S1 0.0337 [-0.5959; 0.6633] 10.9
## S2 -0.0214 [-0.5446; 0.5018] 14.8
## S3 -0.5787 [-1.3128; 0.1555] 8.3
## S4 0.6745 [-0.1048; 1.4539] 7.4
## S5 0.2265 [-0.3774; 0.8305] 11.7
## S6 0.4025 [-0.3214; 1.1264] 8.5
## S7 0.5444 [-0.0754; 1.1641] 11.2
## S8: Conducted by BNU -0.1893 [-0.8121; 0.4335] 11.1
## S9: Albuquerque, et al. (2017) -0.4198 [-1.1460; 0.3064] 8.5
## S10: Only use prompt msgs 0.1888 [-0.5722; 0.9498] 7.8
## intervention
## S1 Gender-stereotype color, ranking, badges, and avatar
## S2 Gender-stereotype color, ranking, badges, and avatar
## S3 Gender-stereotype color, ranking, badges, and avatar
## S4 Gender-stereotype color, ranking, badges, and avatar
## S5 Gender-stereotype color, ranking, badges, and avatar
## S6 Gender-stereotype color, ranking, badges, and avatar
## S7 Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 366
##
## SMD 95%-CI t p-value
## Random effects model 0.0823 [-0.1890; 0.3537] 0.69 0.5098
##
## Quantifying heterogeneity:
## tau^2 = 0.0169 [0.0000; 0.4121]; tau = 0.1299 [0.0000; 0.6420]
## I^2 = 20.0% [0.0%; 60.5%]; H = 1.12 [1.00; 1.59]
##
## Test of heterogeneity:
## Q d.f. p-value
## 11.25 9 0.2587
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## intervention = Gender-stereotype color, rankin ... 9 0.0735 [-0.2336; 0.3805] 0.0318 0.1782 11.17 28.4%
## intervention = Gender-stereotyped motivational ... 1 0.1888 [-0.5722; 0.9498] -- -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.08 1 0.7788
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = `country:age`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for women in stFemale
##
## SMD 95%-CI %W(random) country:age
## S1 0.0337 [-0.5959; 0.6633] 10.9 Brazil:adolescent
## S2 -0.0214 [-0.5446; 0.5018] 14.8 Brazil:adolescent
## S3 -0.5787 [-1.3128; 0.1555] 8.3 Brazil:adolescent
## S4 0.6745 [-0.1048; 1.4539] 7.4 Brazil:adult
## S5 0.2265 [-0.3774; 0.8305] 11.7 Brazil:adult
## S6 0.4025 [-0.3214; 1.1264] 8.5 Brazil:adult
## S7 0.5444 [-0.0754; 1.1641] 11.2 Brazil:adult
## S8: Conducted by BNU -0.1893 [-0.8121; 0.4335] 11.1 China:unknown
## S9: Albuquerque, et al. (2017) -0.4198 [-1.1460; 0.3064] 8.5 Brazil:no-restriction
## S10: Only use prompt msgs 0.1888 [-0.5722; 0.9498] 7.8 Brazil:adolescent
##
## Number of studies combined: k = 10
## Number of observations: o = 366
##
## SMD 95%-CI t p-value
## Random effects model 0.0823 [-0.1890; 0.3537] 0.69 0.5098
##
## Quantifying heterogeneity:
## tau^2 = 0.0169 [0.0000; 0.4121]; tau = 0.1299 [0.0000; 0.6420]
## I^2 = 20.0% [0.0%; 60.5%]; H = 1.12 [1.00; 1.59]
##
## Test of heterogeneity:
## Q d.f. p-value
## 11.25 9 0.2587
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## country:age = Brazil:adolescent 4 -0.0759 [-0.5432; 0.3914] 0 0 2.42 0.0%
## country:age = Brazil:adult 4 0.4401 [ 0.1343; 0.7459] 0 0 0.95 0.0%
## country:age = China:unknown 1 -0.1893 [-0.8121; 0.4335] -- -- 0.00 --
## country:age = Brazil:no-restriction 1 -0.4198 [-1.1460; 0.3064] -- -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 13.94 3 0.0030
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = `country:ed.level`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for women in stFemale
##
## SMD 95%-CI %W(random) country:ed.level
## S1 0.0337 [-0.5959; 0.6633] 10.9 Brazil:upper-secundary
## S2 -0.0214 [-0.5446; 0.5018] 14.8 Brazil:upper-secundary
## S3 -0.5787 [-1.3128; 0.1555] 8.3 Brazil:upper-secundary
## S4 0.6745 [-0.1048; 1.4539] 7.4 Brazil:higher-education
## S5 0.2265 [-0.3774; 0.8305] 11.7 Brazil:higher-education
## S6 0.4025 [-0.3214; 1.1264] 8.5 Brazil:higher-education
## S7 0.5444 [-0.0754; 1.1641] 11.2 Brazil:unknown
## S8: Conducted by BNU -0.1893 [-0.8121; 0.4335] 11.1 China:unknown
## S9: Albuquerque, et al. (2017) -0.4198 [-1.1460; 0.3064] 8.5 Brazil:unknown
## S10: Only use prompt msgs 0.1888 [-0.5722; 0.9498] 7.8 Brazil:upper-secundary
##
## Number of studies combined: k = 10
## Number of observations: o = 366
##
## SMD 95%-CI t p-value
## Random effects model 0.0823 [-0.1890; 0.3537] 0.69 0.5098
##
## Quantifying heterogeneity:
## tau^2 = 0.0169 [0.0000; 0.4121]; tau = 0.1299 [0.0000; 0.6420]
## I^2 = 20.0% [0.0%; 60.5%]; H = 1.12 [1.00; 1.59]
##
## Test of heterogeneity:
## Q d.f. p-value
## 11.25 9 0.2587
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## country:ed.level = Brazil:upper-secundary 4 -0.0759 [-0.5432; 0.3914] 0 0 2.42 0.0%
## country:ed.level = Brazil:higher-education 3 0.3970 [-0.1540; 0.9481] 0 0 0.79 0.0%
## country:ed.level = Brazil:unknown 2 0.0816 [-6.0390; 6.2023] 0.3462 0.5884 3.92 74.5%
## country:ed.level = China:unknown 1 -0.1893 [-0.8121; 0.4335] -- -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 7.24 3 0.0646
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = `country:intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for women in stFemale
##
## SMD 95%-CI %W(random)
## S1 0.0337 [-0.5959; 0.6633] 10.9
## S2 -0.0214 [-0.5446; 0.5018] 14.8
## S3 -0.5787 [-1.3128; 0.1555] 8.3
## S4 0.6745 [-0.1048; 1.4539] 7.4
## S5 0.2265 [-0.3774; 0.8305] 11.7
## S6 0.4025 [-0.3214; 1.1264] 8.5
## S7 0.5444 [-0.0754; 1.1641] 11.2
## S8: Conducted by BNU -0.1893 [-0.8121; 0.4335] 11.1
## S9: Albuquerque, et al. (2017) -0.4198 [-1.1460; 0.3064] 8.5
## S10: Only use prompt msgs 0.1888 [-0.5722; 0.9498] 7.8
## country:intervention
## S1 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S2 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S3 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S4 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S5 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S6 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S7 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU China:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) Brazil:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs Brazil:Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 366
##
## SMD 95%-CI t p-value
## Random effects model 0.0823 [-0.1890; 0.3537] 0.69 0.5098
##
## Quantifying heterogeneity:
## tau^2 = 0.0169 [0.0000; 0.4121]; tau = 0.1299 [0.0000; 0.6420]
## I^2 = 20.0% [0.0%; 60.5%]; H = 1.12 [1.00; 1.59]
##
## Test of heterogeneity:
## Q d.f. p-value
## 11.25 9 0.2587
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau
## country:intervention = Brazil:Gender-stereotype color, ... 8 0.1091 [-0.2395; 0.4577] 0.0434 0.2082
## country:intervention = China:Gender-stereotype color, ... 1 -0.1893 [-0.8121; 0.4335] -- --
## country:intervention = Brazil:Gender-stereotyped motiv ... 1 0.1888 [-0.5722; 0.9498] -- --
## Q I^2
## country:intervention = Brazil:Gender-stereotype color, ... 10.40 32.7%
## country:intervention = China:Gender-stereotype color, ... 0.00 --
## country:intervention = Brazil:Gender-stereotyped motiv ... 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.83 2 0.6604
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = `age:intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for women in stFemale
##
## SMD 95%-CI %W(random)
## S1 0.0337 [-0.5959; 0.6633] 10.9
## S2 -0.0214 [-0.5446; 0.5018] 14.8
## S3 -0.5787 [-1.3128; 0.1555] 8.3
## S4 0.6745 [-0.1048; 1.4539] 7.4
## S5 0.2265 [-0.3774; 0.8305] 11.7
## S6 0.4025 [-0.3214; 1.1264] 8.5
## S7 0.5444 [-0.0754; 1.1641] 11.2
## S8: Conducted by BNU -0.1893 [-0.8121; 0.4335] 11.1
## S9: Albuquerque, et al. (2017) -0.4198 [-1.1460; 0.3064] 8.5
## S10: Only use prompt msgs 0.1888 [-0.5722; 0.9498] 7.8
## age:intervention
## S1 adolescent:Gender-stereotype color, ranking, badges, and avatar
## S2 adolescent:Gender-stereotype color, ranking, badges, and avatar
## S3 adolescent:Gender-stereotype color, ranking, badges, and avatar
## S4 adult:Gender-stereotype color, ranking, badges, and avatar
## S5 adult:Gender-stereotype color, ranking, badges, and avatar
## S6 adult:Gender-stereotype color, ranking, badges, and avatar
## S7 adult:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU unknown:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) no-restriction:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs adolescent:Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 366
##
## SMD 95%-CI t p-value
## Random effects model 0.0823 [-0.1890; 0.3537] 0.69 0.5098
##
## Quantifying heterogeneity:
## tau^2 = 0.0169 [0.0000; 0.4121]; tau = 0.1299 [0.0000; 0.6420]
## I^2 = 20.0% [0.0%; 60.5%]; H = 1.12 [1.00; 1.59]
##
## Test of heterogeneity:
## Q d.f. p-value
## 11.25 9 0.2587
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q
## age:intervention = adolescent:Gender-stereotype co ... 3 -0.1328 [-0.8798; 0.6141] <0.0001 0.0007 1.86
## age:intervention = adult:Gender-stereotype color, ... 4 0.4401 [ 0.1343; 0.7459] 0 0 0.95
## age:intervention = unknown:Gender-stereotype color ... 1 -0.1893 [-0.8121; 0.4335] -- -- 0.00
## age:intervention = no-restriction:Gender-stereotyp ... 1 -0.4198 [-1.1460; 0.3064] -- -- 0.00
## age:intervention = adolescent:Gender-stereotyped m ... 1 0.1888 [-0.5722; 0.9498] -- -- 0.00
## I^2
## age:intervention = adolescent:Gender-stereotype co ... 0.0%
## age:intervention = adult:Gender-stereotype color, ... 0.0%
## age:intervention = unknown:Gender-stereotype color ... --
## age:intervention = no-restriction:Gender-stereotyp ... --
## age:intervention = adolescent:Gender-stereotyped m ... --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 13.96 4 0.0074
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = `ed.level:intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for women in stFemale
##
## SMD 95%-CI %W(random)
## S1 0.0337 [-0.5959; 0.6633] 10.9
## S2 -0.0214 [-0.5446; 0.5018] 14.8
## S3 -0.5787 [-1.3128; 0.1555] 8.3
## S4 0.6745 [-0.1048; 1.4539] 7.4
## S5 0.2265 [-0.3774; 0.8305] 11.7
## S6 0.4025 [-0.3214; 1.1264] 8.5
## S7 0.5444 [-0.0754; 1.1641] 11.2
## S8: Conducted by BNU -0.1893 [-0.8121; 0.4335] 11.1
## S9: Albuquerque, et al. (2017) -0.4198 [-1.1460; 0.3064] 8.5
## S10: Only use prompt msgs 0.1888 [-0.5722; 0.9498] 7.8
## ed.level:intervention
## S1 upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S2 upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S3 upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S4 higher-education:Gender-stereotype color, ranking, badges, and avatar
## S5 higher-education:Gender-stereotype color, ranking, badges, and avatar
## S6 higher-education:Gender-stereotype color, ranking, badges, and avatar
## S7 unknown:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU unknown:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) unknown:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs upper-secundary:Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 366
##
## SMD 95%-CI t p-value
## Random effects model 0.0823 [-0.1890; 0.3537] 0.69 0.5098
##
## Quantifying heterogeneity:
## tau^2 = 0.0169 [0.0000; 0.4121]; tau = 0.1299 [0.0000; 0.6420]
## I^2 = 20.0% [0.0%; 60.5%]; H = 1.12 [1.00; 1.59]
##
## Test of heterogeneity:
## Q d.f. p-value
## 11.25 9 0.2587
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau
## ed.level:intervention = upper-secundary:Gender-stereoty ... 3 -0.1328 [-0.8798; 0.6141] <0.0001 0.0007
## ed.level:intervention = higher-education:Gender-stereot ... 3 0.3970 [-0.1540; 0.9481] 0 0
## ed.level:intervention = unknown:Gender-stereotype color ... 3 -0.0029 [-1.2549; 1.2491] 0.1440 0.3795
## ed.level:intervention = upper-secundary:Gender-stereoty ... 1 0.1888 [-0.5722; 0.9498] -- --
## Q I^2
## ed.level:intervention = upper-secundary:Gender-stereoty ... 1.86 0.0%
## ed.level:intervention = higher-education:Gender-stereot ... 0.79 0.0%
## ed.level:intervention = unknown:Gender-stereotype color ... 4.59 56.5%
## ed.level:intervention = upper-secundary:Gender-stereoty ... 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 6.51 3 0.0893
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = `country:age:intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for women in stFemale
##
## SMD 95%-CI %W(random)
## S1 0.0337 [-0.5959; 0.6633] 10.9
## S2 -0.0214 [-0.5446; 0.5018] 14.8
## S3 -0.5787 [-1.3128; 0.1555] 8.3
## S4 0.6745 [-0.1048; 1.4539] 7.4
## S5 0.2265 [-0.3774; 0.8305] 11.7
## S6 0.4025 [-0.3214; 1.1264] 8.5
## S7 0.5444 [-0.0754; 1.1641] 11.2
## S8: Conducted by BNU -0.1893 [-0.8121; 0.4335] 11.1
## S9: Albuquerque, et al. (2017) -0.4198 [-1.1460; 0.3064] 8.5
## S10: Only use prompt msgs 0.1888 [-0.5722; 0.9498] 7.8
## country:age:intervention
## S1 Brazil:adolescent:Gender-stereotype color, ranking, badges, and avatar
## S2 Brazil:adolescent:Gender-stereotype color, ranking, badges, and avatar
## S3 Brazil:adolescent:Gender-stereotype color, ranking, badges, and avatar
## S4 Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S5 Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S6 Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S7 Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU China:unknown:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) Brazil:no-restriction:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs Brazil:adolescent:Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 366
##
## SMD 95%-CI t p-value
## Random effects model 0.0823 [-0.1890; 0.3537] 0.69 0.5098
##
## Quantifying heterogeneity:
## tau^2 = 0.0169 [0.0000; 0.4121]; tau = 0.1299 [0.0000; 0.6420]
## I^2 = 20.0% [0.0%; 60.5%]; H = 1.12 [1.00; 1.59]
##
## Test of heterogeneity:
## Q d.f. p-value
## 11.25 9 0.2587
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau
## country:age:intervention = Brazil:adolescent:Gender-stereo ... 3 -0.1328 [-0.8798; 0.6141] <0.0001 0.0007
## country:age:intervention = Brazil:adult:Gender-stereotype ... 4 0.4401 [ 0.1343; 0.7459] 0 0
## country:age:intervention = China:unknown:Gender-stereotype ... 1 -0.1893 [-0.8121; 0.4335] -- --
## country:age:intervention = Brazil:no-restriction:Gender-st ... 1 -0.4198 [-1.1460; 0.3064] -- --
## country:age:intervention = Brazil:adolescent:Gender-stereo ... 1 0.1888 [-0.5722; 0.9498] -- --
## Q I^2
## country:age:intervention = Brazil:adolescent:Gender-stereo ... 1.86 0.0%
## country:age:intervention = Brazil:adult:Gender-stereotype ... 0.95 0.0%
## country:age:intervention = China:unknown:Gender-stereotype ... 0.00 --
## country:age:intervention = Brazil:no-restriction:Gender-st ... 0.00 --
## country:age:intervention = Brazil:adolescent:Gender-stereo ... 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 13.96 4 0.0074
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = `country:ed.level:intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for women in stFemale
##
## SMD 95%-CI %W(random)
## S1 0.0337 [-0.5959; 0.6633] 10.9
## S2 -0.0214 [-0.5446; 0.5018] 14.8
## S3 -0.5787 [-1.3128; 0.1555] 8.3
## S4 0.6745 [-0.1048; 1.4539] 7.4
## S5 0.2265 [-0.3774; 0.8305] 11.7
## S6 0.4025 [-0.3214; 1.1264] 8.5
## S7 0.5444 [-0.0754; 1.1641] 11.2
## S8: Conducted by BNU -0.1893 [-0.8121; 0.4335] 11.1
## S9: Albuquerque, et al. (2017) -0.4198 [-1.1460; 0.3064] 8.5
## S10: Only use prompt msgs 0.1888 [-0.5722; 0.9498] 7.8
## country:ed.level:intervention
## S1 Brazil:upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S2 Brazil:upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S3 Brazil:upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S4 Brazil:higher-education:Gender-stereotype color, ranking, badges, and avatar
## S5 Brazil:higher-education:Gender-stereotype color, ranking, badges, and avatar
## S6 Brazil:higher-education:Gender-stereotype color, ranking, badges, and avatar
## S7 Brazil:unknown:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU China:unknown:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) Brazil:unknown:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs Brazil:upper-secundary:Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 366
##
## SMD 95%-CI t p-value
## Random effects model 0.0823 [-0.1890; 0.3537] 0.69 0.5098
##
## Quantifying heterogeneity:
## tau^2 = 0.0169 [0.0000; 0.4121]; tau = 0.1299 [0.0000; 0.6420]
## I^2 = 20.0% [0.0%; 60.5%]; H = 1.12 [1.00; 1.59]
##
## Test of heterogeneity:
## Q d.f. p-value
## 11.25 9 0.2587
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ... 3 -0.1328 [-0.8798; 0.6141] <0.0001
## country:ed.level:intervention = Brazil:higher-education:Gender- ... 3 0.3970 [-0.1540; 0.9481] 0
## country:ed.level:intervention = Brazil:unknown:Gender-stereotyp ... 2 0.0816 [-6.0390; 6.2023] 0.3462
## country:ed.level:intervention = China:unknown:Gender-stereotype ... 1 -0.1893 [-0.8121; 0.4335] --
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ... 1 0.1888 [-0.5722; 0.9498] --
## tau Q I^2
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ... 0.0007 1.86 0.0%
## country:ed.level:intervention = Brazil:higher-education:Gender- ... 0 0.79 0.0%
## country:ed.level:intervention = Brazil:unknown:Gender-stereotyp ... 0.5884 3.92 74.5%
## country:ed.level:intervention = China:unknown:Gender-stereotype ... -- 0.00 --
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ... -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 7.50 4 0.1117
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.cont <- update.meta(m.cont, studlab = data$study)
summary(eggers.test(x = m.cont))## Eggers' test of the intercept
## =============================
##
## intercept 95% CI t p
## 0.397 -5.54 - 6.33 0.131 0.9
##
## Eggers' test does not indicate the presence of funnel plot asymmetry.funnel(m.cont, xlab = "Hedges' g", studlab = T, legend=T, addtau2 = T)